Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
- Cite this paper as:
- Bartlett P.L., Mendelson S. (2001) Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. In: Helmbold D., Williamson B. (eds) Computational Learning Theory. COLT 2001. Lecture Notes in Computer Science, vol 2111. Springer, Berlin, Heidelberg
We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes.We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.
Unable to display preview. Download preview PDF.